The recalibration of our relationships with science (and nature) by natural hazard risk mitigation practitioners

2021 ◽  
pp. 251484862110198
Author(s):  
Jessica K Weir ◽  
Timothy Neale ◽  
Elizabeth A Clarke

Unrealistic expectations in society about science reducing and even eliminating the risk of natural hazards contrasts with the chaotic forces of these events, but such expectations persist nonetheless. Risk mitigation practitioners must grapple with them, including in the cycles of blame and inquiry that follow natural hazard events. We present a synthesis of such practitioner experiences from three consequential bushfire and flood risk landscapes in Australia in which science was being used to change policy and/or practice. We show how they chose to work with, counter and recalibrate unrealistic expectations of science, as well as embrace socionatural complexity and a consequential nature. The mismatch between the challenges faced by the sector and the unrealistic expectations of science, generated more stressful work conditions, less effective risk mitigation, and less effective use of research monies. In response, we argue for structural and procedural change to address legacy pathways that automatically privilege science, especially in relation to nature, with broader relevance for other environmental issues. This is not to dismiss or debase science, but to better understand its use and utility, including how facts and values relate.

Author(s):  
S. Phoompanich ◽  
S. Barr ◽  
R. Gaulton

<p><strong>Abstract.</strong> In order to mitigate environmental risk in Thailand it is essential to understand where and when specific geographic areas will be exposed to individual and multiple natural hazards. However, existing national scale approaches to natural hazard risk assessment are poorly adapted to deal with multiple hazards where significant uncertainties are associated with input variables and prior knowledge of the spatiotemporal nature of hazards is limited. To overcome these limitations, a geospatial approach has been developed that integrates machine learning within a GIS environment. Four hazards were investigated by Naïve Bayes while multiple hazards and their causalities were analysed via a Bayesian Network. Geospatial and Earth observation data representing past hazard events and their trigger variables were analysed to derive the probability of a hazard. Results revealed that lowland areas covering 22,868 and 139,193 km<sup>2</sup>, or 5% and 29% of total lowland areas were at-risk at a 90% probability-level of floods in rainy-seasons and droughts in the summer. High mountains and the plateaus were exposed to landslides over 90% probability in rainy, and forest fires in summer with over 60% probability, covering 37,727 and 40,069 km<sup>2</sup>, respectively. Within the Bayesian Network four relations of multiple hazards were investigated. At a 90% significance level approximately 190,250 km2 was at risk from a combination of forest fires and droughts. At a 80% or greater probability, 161,450, 120,027, and 102,628 km<sup>2</sup> of land were at risk from a combination of 1) floods and landslides, 2) forest fires, floods, and landslides, and 3) all four hazards, respectively. The results were then used to produce the first fine-spatial scale multi-hazard assessment to support national policies on risk mitigation.</p>


2017 ◽  
Vol 25 (3) ◽  
pp. 21-46 ◽  
Author(s):  
Hyungjun Park ◽  
Gyoungjun Ha ◽  
Dalbyul Lee ◽  
Juchul Jung

2005 ◽  
Vol 29 (3) ◽  
pp. 493-509 ◽  
Author(s):  
Lorena Montoya ◽  
Ian Masser

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Miroslav Nastev ◽  
Marie-José Nollet ◽  
Ahmad Abo El Ezz ◽  
Alex Smirnoff ◽  
Sarah Kate Ploeger ◽  
...  

2017 ◽  
Vol 21 ◽  
pp. 176-186 ◽  
Author(s):  
Jim McLennan ◽  
Danielle Every ◽  
Christopher Bearman ◽  
Lyndsey Wright
Keyword(s):  

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